Method and apparatus for automatic cancer diagnosis scoring of tissue samples

ABSTRACT

Certain aspects of an apparatus and method for automatic ER/PR scoring of tissue samples may include for determining a cancer diagnosis score comprising identifying a positive stained nucleus in a slide image of the tissue sample, identifying a negative stained nucleus in the slide image, computing a proportion score based on number of the positive stained nucleus identified and number of the negative stained nucleus identified and determining the cancer diagnosis score based on the proportion.

CROSS-REFERENCE TO RELATED APPLICATIONS/INCORPORATION BY REFERENCE

This application makes reference to:

Commonly assigned and Co-pending U.S. patent application Ser. No.13/549,019 filed on Jul. 13, 2012.

Each of the above referenced applications is hereby incorporated hereinby reference in its entirety.

FIELD

Certain embodiments of the invention relate to identifying cancerthrough digital pathology. More specifically, certain embodiments of theinvention relate to a method and apparatus for automatic cancerdiagnosis scoring of tissue samples.

BACKGROUND

In the area of biology and medicine, understanding cells and theirsupporting structures in tissues, tracking their structure anddistribution changes are very important. Histology, the study of themicroscopic anatomy of tissues, is essential in disease diagnosis,medicinal development and many other fields. In histology, the processof examining a thin slice of tissue sample under a light microscope orelectron microscope is generally performed. In order to visualize anddifferentiate various microscopic biological substances, one commonapproach is to stain the tissue sample with a combination of severaldyes that have selective responses to the presence of differentbiological substances. In doing so, specified biological substances suchas nuclei, cytoplasm, membranes, disease markers, specific proteins andother structures, are visually enhanced, thereby facilitating detectionand localization of these microscopic biological substances. In manyinstances diseased tissues present specific anatomical and physiologicalalterations that can be detected using histological analysis.

Various cancer cells contain proteins or other biomolecules that areeither absent or expressed at a different level in normal tissue. Suchproteins or biomolecules are referred to as cancer markers. Cancermarkers are typically used for diagnosis and/or targeted therapy. Forexample, estrogen receptors (ER) and progesterone receptors (PR) aregenerally accepted as markers for breast cancer. In many instances,tests for receptor status, such as status of estrogen receptors (ER) andprogesterone receptors (PR) of breast cancer tissue is performed foridentifying an effective chemotherapy regimen. The receptor status maybe determined using an immunohistochemistry (IHC) staining process thatinvolves use of receptor specific stains to stain cancer cells so as toaid in their visualization.

ER or PR IHC test results are represented by an ER/PR “score”.Currently, the process of generating an ER/PR “score” is often based onthe manual visual analysis of an examining pathologist. Currentautomatic scoring techniques provide inconsistent results due toimproper identification of stained cancer cells. Further, the process istime consuming and results are often inconsistent.

Therefore there is a need in the art for an efficient method andapparatus for identification of stained cancer cells in automatic ER/PRscoring of tissue samples.

Further limitations and disadvantages of conventional and traditionalapproaches will become apparent to one of skill in the art, throughcomparison of such systems with some aspects of the present invention asset forth in the remainder of the present application and with referenceto the drawings.

SUMMARY

An apparatus and method is provided for automatic estrogen receptor orprogesterone receptor (ER/PR) scoring of tissue samples substantially asshown in and/or described in connection with at least one of thefigures, as set forth more completely in the claims.

These and other features and advantages of the present invention may beappreciated from a review of the following detailed description of thepresent invention, along with the accompanying figures in which likereference numerals refer to like parts throughout.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a cancer diagnosis scoringmodule, in accordance with an exemplary embodiment of the presentinvention;

FIG. 2 is a functional block diagram of a region module in accordancewith exemplary embodiments of the present invention;

FIG. 3 is a functional block diagram of a nucleus size estimation modulein accordance with exemplary embodiments of the present invention;

FIG. 4 is a functional block diagram of a negative nucleusidentification module in accordance with exemplary embodiments of thepresent invention;

FIG. 5 is a functional block diagram of a positive nucleusidentification module in accordance with exemplary embodiments of thepresent invention;

FIG. 6 is a functional block diagram of a proportion score module inaccordance with exemplary embodiments of the present invention;

FIG. 7 is a block diagram of a computer system for implementing thescoring module in accordance with embodiments of the present invention;

FIG. 8 is an illustration of a flow diagram for a method for determininga diagnosis score according to exemplary embodiments of the presentinvention;

FIG. 9 is an illustration of a flow diagram for a method for estimatingnucleus size according to exemplary embodiments of the presentinvention;

FIG. 10 is an illustration of a flow diagram for a method foridentifying negative nuclei in a negative stain image according toexemplary embodiments of the present invention;

FIG. 11 is an illustration of a flow diagram for a method foridentifying positive nuclei in a positive stain image according toexemplary embodiments of the present invention;

FIG. 12 is an illustration of a flow diagram for a method fordetermining a proportion score for a slide image according to exemplaryembodiments of the present invention; and

FIG. 13 is an illustration of an ER/PR score table according toexemplary embodiments of the present invention.

DETAILED DESCRIPTION

Embodiments of the invention relate to an apparatus and/or method forautomatic cancer diagnosis scoring of tissue samples. According to oneembodiment, a salient region is selected from an input slide containingtissue samples stained with red and blue dyes, where the salient regionis based on either user input or algorithmic selection, to find theregion which is rich in both the red and blue channels of the image.Stain vectors of the salient region are calculated. Additionally,Optical Domain transformations are applied to a user-selected interestarea in the input slide, and then stain separation is performed on thetransformed area into a positive stain image and a negative stain image.The stain intensity of pixels of the positive stain image and thenegative stain image are each analyzed to determine a pixel count foreach stain image, which pixel counts are used for obtaining a size rangeparameter An average intensity of the positive stain is determined usingthe positive stain image. Positive stained nuclei in the positive stainimage are identified using the size range parameter and the averageintensity of the positive stain. Negative stained nuclei in the negativestain image are identified using the size range parameter. A totalnumber of identified nuclei is computed by summing the number ofidentified positive stained nuclei and the number of identified negativestained nuclei. A proportion score (PS) is computed based on thepercentage of identified positive stained nuclei compared to the totalnumber of identified nuclei, and an intensity score (IS) is obtainedbased on the average intensity of the identified positive stainednuclei. The final ER/PR score is computed by summing the PS and ISscores.

FIG. 1 is a block diagram illustrating an estrogen receptor orprogesterone receptor (ER/PR) scoring module 100, in accordance with anembodiment of the invention. The ER/PR scoring module 100 comprises aregion module 102, a nucleus size estimation module 106, a positivenucleus identification module 108, a negative nucleus identificationmodule 110 and a proportion scoring module 112. The scoring module 100takes an input digital slide image 101 as an input. According to oneembodiment, the scoring module 100 is directly coupled to an electronmicroscope (not shown), or other device through which slides of tissuesamples are viewed and the digital slide image 101 is generated.

FIG. 2 is a functional block diagram of the region module 102 inaccordance with exemplary embodiments of the present invention. Theregion module 102 comprises a salient region module 202, a stain vectormodule 204, an interest area module 206 and a separation module 208.According to one embodiment, the ER/PR scoring module 100 of FIG. 1passes the input slide image 101 to the salient region module 202 andthe interest area module 206 in parallel.

The salient region module 202 is coupled to the stain vector module 204.The salient region module 202 analyzes the input slide image 101 anddetermines that a particular region of the slide image 101 is a salientregion for analysis. According to one embodiment, saliency is determinedby an automated color analysis technique that finds the region of theinput image 101 that is rich in both red and blue channels. Richness ofa particular color is indicated by the amount of pixels appearing inthat particular color, such as red, and the intensity in the red channelwill be higher than other channels. Richness in the blue channel isindicated by there being many pixels appearing to be blue, and theintensity in the blue channel of the image 101 will be higher than otherchannels. In some embodiments, if the input slide image 101 isdetermined to be smaller than a threshold size, the entire image isdetermined to be a salient region. The stain vector module 204 takes thesalient region as input and calculates stain vectors 220 of the region,as disclosed in commonly assigned related pending U.S. patentapplication Ser. No. 13/549,019. Further details of that particularprocess are not needed to understand the present invention andtherefore, in the interest of clarity, are not provided.

In parallel, the interest area module 206 extracts a user interest areafrom the input slide image 101. The image 101 is transformed from ared/green/blue (RGB) domain to an optical domain as described incommonly assigned co-pending U.S. patent application Ser. No.13/549,019. The separation module 208 takes the optical domain image asinput and separates the optical domain image into a positive stain image224 and a negative stain image 226. According to some embodiments, thepositive stain image 224 provides positive stain acquired by the imageslide and the negative stain image 226 provides negative stain acquiredby the image slide.

FIG. 3 is a functional block diagram of the nucleus size estimationmodule 106 (NSEM) in accordance with exemplary embodiments of thepresent invention. The NSEM 106 takes the positive stain image 224 andthe negative stain image 226 as input. According to some embodiments,the NSEM 106 counts negative stained pixels in the negative stain image226 and positive stained pixels in the positive stain image 224respectively to obtain a size range parameter 310 based on a ratio ofthe counted number of positive stained pixels to the counted number ofnegative stained pixels. The size of a stained nucleus differentiatesthe nucleus from other objects in the stained image. According to someembodiments, the size range parameter is generally larger than thennucleus.

FIG. 4 is a functional block diagram of the negative nucleusidentification module 110 in accordance with exemplary embodiments ofthe present invention. The NNIM 110 comprises a blob identificationmodule 402 and a distance masking module 404. The distance maskingmodule 404 further comprises a filter 406 and a transform module 408.The NNIM 110 receives as input the negative stain image 226 from theregion module 102 of FIG. 1 as well as, in some embodiments, the sizerange parameter 310, and outputs negative stained nucleus identificationinformation 410.

According to an exemplary embodiment, the blob identification module 402applies a window averaging procedure to the intensity level of eachpixel P of the negative stain image 226 according to the followingexpression:

$\overset{\rightharpoonup}{P\left( {\iota,j} \right)} = {\frac{1}{S \times S}{\sum\limits_{i = 1}^{S}\;{\sum\limits_{j = 1}^{S}\;{P\left( {{i - \frac{S}{2}},{j - \frac{S}{2}}} \right)}}}}$where S is the length of the window and “I” and “j” iterate over lengthS. According to some embodiments S is obtained as the size rangeparameter 310 from the NSEM 106. After all pixels of the negative stainimage 226 are processed, an image inverse process is performed on eachpixel P, as follows:{right arrow over (P(i,j))}=255−{right arrow over (P(i,j))}After the inversion, each pixel of the negative stain image 226 iscompared to a negative nucleus threshold value to determine if thecurrent pixel is a potential negative stained nucleus center. If thepixel value is greater than the negative nucleus threshold, the currentpixel is identified as a potential negative stained nucleus center atoutput 409; otherwise, the pixel is excluded as a potential negativestained nucleus center.

A low-pass filter, i.e. the filter 406, is applied to the negative stainimage 226 received as an input to the distance masking module 404. Thefilter 406 removes noise from the negative stain image 226, andsubsequently a binary image generation process is applied to each pixelof the low-pass filtered image according to the following formula togenerate a binary image at output 411:

${B\left( {i,j} \right)} = \left\{ \begin{matrix}1 & {{{if}\mspace{14mu}{P\left( {i,j} \right)}} < {T\; 1}} \\0 & {{{if}\mspace{14mu}{P\left( {i,j} \right)}} \geq {T\; 1}}\end{matrix} \right.$where T1 is a threshold value that determines if a current pixel hasstrong enough negative stain to be qualified as a potential negativestained nuclei pixel. The lower the value of the pixel, the stronger thenegative stain is.

The transform module 408 receives as an input the binary image B(i,j)from output 411 and applies a distance transform thereto to calculatethe minimum distance of each white pixel (value of 1) to any black pixel(value of 0). Once the distance transform is complete, a distance imageis generated using the distance value as the pixel value. The distancemasking module 404 also receives as an input, from output 409 of theblob identification module 402, pixels of the negative stained image 226identified as the potential negative stained nucleus centers. For eachpotential negative stained nucleus center pixel identified in the blobidentification module 402, the corresponding distance value in the samelocation of the distance image is added onto the potential negativestained nucleus center pixel to generate a new image according to thefollowing equation:

${P_{new}\left( {i,j} \right)} = \left\{ \begin{matrix}{\overset{\rightharpoonup}{P\left( {\iota,J} \right)} + {D\left( {i,j} \right)}} & {{if}\mspace{14mu}\overset{\rightharpoonup}{{P\left( {\iota,J} \right)}\mspace{11mu}}\;{is}\mspace{14mu} a\mspace{14mu}{potential}\mspace{14mu}{nucleus}\mspace{14mu}{center}} \\0 & {Otherwise}\end{matrix} \right.$Each pixel in the newly generated image is compared to its neighbors andthe local maximum pixel is identified as a negative stained nucleuscenter. The S×S area surrounding it is identified as the negativestained nucleus area and output as negative stained nucleusidentification information 410.

FIG. 5 is a functional block diagram of the positive nucleusidentification module (PNIM) 108 of FIG. 1 in accordance with exemplaryembodiments of the present invention. The PNIM 108 comprises anintensity analysis module 502 and a blob identification module 504. ThePNIM 108 receives the positive stain image 224 from the region module102 as input, and outputs the average intensity 510 of positive stain inthe positive stain image 224 and positive stained nucleus identificationinformation 520 is identified.

According to an exemplary embodiment, the intensity analysis module 502calculates the average intensity 510 according to the followingexpression:

${Average}_{intensity} = {\frac{1}{M}{\sum\limits_{{P{({i,j})}} < {T\; 2}}^{\;}\;{P\left( {i,j} \right)}}}$where M is total number of pixels that satisfy the condition: P(i,j)<T2where T2 is a predetermined threshold value. For example, in oneembodiment, T2 is 200.

The blob identification module 504 applies a window averaging procedureto each pixel of the positive stain image 224 according to the followingexpression:

$\overset{\rightharpoonup}{P\left( {\iota,J} \right)} = {\frac{1}{S \times S}{\sum\limits_{i = 1}^{S}\;{\sum\limits_{j = 1}^{S}\;{P\left( {{i - \frac{S}{2}},{j - \frac{S}{2}}} \right)}}}}$where S is the length of the window. According to some embodiments S isobtained as the size range parameter 310 from the NSEM 106. After allpixels of the positive stain image 224 are processed, an image inverseprocess is performed:{right arrow over (P(i,j))}=255−{right arrow over (P(i,j))}After the inversion, each pixel of the positive stain image 224 iscompared to a content adaptive positive nucleus threshold value T3 todetermine if the current pixel is a potential positive stained nucleuscenter. If the pixel value is greater than the positive nucleusthreshold, the current pixel is identified as a potential positivestained nucleus center; otherwise, the pixel is excluded as a potentialpositive stained nucleus center.

According to some embodiments the content adaptive positive nucleusthreshold T3 is based on the average intensity 510 and is computedaccording to the following expression:T3=C×(255−Average_(intensity))where T3 is the positive nucleus threshold and C is a constantparameter. For example, C may be 20. The local maximum pixel isidentified as a positive stained nucleus center. The S×S areasurrounding it is identified as the positive stained nucleus area andoutput as positive stained nucleus identification information 520.

FIG. 6 is a functional block diagram of a proportion score (PS) module112 in accordance with exemplary embodiments of the present invention.The PS module 112 takes as input the negative nucleus identificationinformation 410 and the positive nucleus identification information 520from the NNIM 110 and the PNIM 108 respectively. According to someembodiments, the PS module 112 computes the number of negative stainednucleus identified in the negative stain image 226 according to thenegative nucleus identification information 410 and the number ofpositive stained nucleus identified in the positive stain image 224according to the positive nucleus identification information 520.

According to some embodiments, the PS module 112 computes a total nucleicount by summing number of the positive stained nucleus identified andthe negative stained nucleus identified. Further the PS module 112computes a positive stained nuclei percentage as a proportion score 610based on number of the positive stained nucleus identified and the totalnuclei count.

FIG. 7 is a block diagram of a computer system 700 for implementing thescoring module 100 of FIG. 1 in accordance with embodiments of thepresent invention. The system 700 includes a processor 702, a memory 704and various support circuits 706. The processor (i.e. CPU) 702 mayinclude one or more microprocessors known in the art, and/or dedicatedfunction processors such as field programmable gate arrays programmed toperform dedicated processing functions. The support circuits 706 for theprocessor 702 include microcontrollers, application specific integratedcircuits (ASIC), cache, power supplies, clock circuits, data registers,input/output (I/O) interface 708, and the like. The I/O interface 708may be directly coupled to the memory 704 or coupled through thesupporting circuits 706. The I/O interface 708 may also be configuredfor communication with input devices and/or output devices 710, such as,network devices, various storage devices, mouse, keyboard, displays,sensors and the like.

The memory 704 stores non-transient processor-executable instructionsand/or data that may be executed by and/or used by the processor 702.These processor-executable instructions may comprise firmware, software,and the like, or some combination thereof. Modules havingprocessor-executable instructions that are stored in the memory 704comprise the scoring module 720, further comprising the region module722, the nucleus size estimation module 726, the positive nucleusidentification module (PNIM) 728, the negative nucleus identification(NNIM) 730 and a proportion scoring module 732.

The computer 700 may be programmed with one or more operating systems(generally referred to as operating system (OS) 714, which may includeOS/2, Java Virtual Machine, Linux, Solaris, Unix, HPUX, AIX, Windows,Windows95, Windows98, Windows NT, and Windows 2000, Windows ME, WindowsXP, Windows Server, among other known platforms. At least a portion ofthe operating system 714 may be disposed in the memory 704. In anexemplary embodiment, the memory 704 may include one or more of thefollowing: random access memory, read only memory, magneto-resistiveread/write memory, optical read/write memory, cache memory, magneticread/write memory, and the like, as well as signal-bearing media, notincluding non-transitory signals such as carrier waves and the like.

The region module 722 further comprises the salient region module 752,the stain vector module 754, the interest area module 756 and theseparation module 758. The negative nucleus identification module 730further comprises the blob identification module 762 and the distancemasking module 764. The positive nucleus identification module 728further comprises the intensity analysis module 772 and the blobidentification module 774.

FIG. 8 is an illustration of a flow diagram for a method 800 fordetermining a diagnosis score according to exemplary embodiments of thepresent invention. The method 800 is an implementation of the scoringmodule 100 shown in FIG. 1, executed as scoring module 720 by theprocessor 702. The method begins at step 802 and proceeds to step 804.

At step 804, the salient region module 202 identifies the salientregions of an input image (e.g., an input slide image of a stainedtissue sample). Within step 804, the method 800 performs step 804 a,wherein saliency analysis is applied to the slide image, whichdetermines the saliency of a region based on the existence of red andblue channels in a stain.

The method then proceeds to step 806, where stain separation isperformed on the salient region by the separation module 208. Stainseparation is performed in step 806 a, where stain vectors arecalculated for the stains. At step 808, the size range parameter 310 isdetermined by the NSEM 106 using the positive stain image 224 and thenegative stain image 226. At step 810, the average intensity 510 ofpositive stain in the positive stain image 226 is determined by theaverage intensity module 502.

At step 812, positive stained nuclei information is determined by thePNIM 728 using the average intensity 510 and the size range parameter310. At step 814, negative stained nuclei information is determined bythe NNIM 730 using the size range parameter 310 and distance masking asdescribed in conjunction with FIG. 4. At step 816, scoring module 720determines a cancer diagnosis score based on number of negative stainednucleus identified, number of positive stained nucleus identified andthe average intensity. The method 800 then ends at step 818.

According to some embodiments the scoring module 720 provides an ER/PRscore based on the scoring guideline provided by the American Society ofClinical Oncology and the College of American Pathologists as describedin detail below with reference to FIG. 13.

FIG. 9 is an illustration of a flow diagram for a method 900 forestimating nucleus size according to exemplary embodiments of thepresent invention. The method 900 is an implementation of the nucleussize estimation module (NSEM) 106 shown in FIG. 1, implemented as theNSEM 726 in FIG. 7 as executed by the processor 702. The method 900begins at step 902 and proceeds to step 904.

At step 904, the negative stain image 226 is taken by the NSEM 106 asinput. At step 906, determination of total number of negative stainedpixels below a predetermined negative threshold in the negative stainimage 226 is performed by the NSEM 726. At step 908, the positive stainimage 224 is taken by the NSEM 726 as input. At step 910, determinationof total number of positive stained pixels below a predeterminedpositive threshold in the positive stain image 224 is performed by theNSEM 726.

At step 912, the size range parameter 310 is obtained. According to someembodiments, the NSEM 106 obtains the size range parameter as:

$S = {f\left( \frac{N_{positive}}{N_{negative}} \right)}$where N_(positive) is total number of positive stained pixels in thepositive stain image 224 below a predetermined positive threshold,N_(negative) is total number of negative stained pixels in the negativestain image 226 below a predetermined positive threshold, and f(x) is afunction proportional to the value of x, where x denotes the inputparameter of the function f( ). The method 900 then terminates at step914.

FIG. 10 is an illustration of a flow diagram for a method 1000 foridentifying negative nuclei in a negative stain image according toexemplary embodiments of the present invention. The method 1000 is animplementation of the negative nucleus identification module (NNIM) 110shown in FIG. 1, implemented as the NNIM 730 in FIG. 7 as executed bythe processor 702. The method 1000 begins at step 1002 and proceeds tostep 1004. Steps 1004-1012 are executed significantly in parallel tosteps 1014-1016 according to one embodiment of the present invention.

First, steps 1004-1012 will be discussed. At step 1004, a windowaveraging procedure is applied to the negative stain image produced bythe earlier stain separation, by the blob identification module 762. Thepixel values obtained through the window averaging are then inverted atstep 1006 and compared to a predetermined negative nucleus thresholdpixel value at step 1008. If the value at the current pixel is notgreater than the negative nucleus threshold value, the current pixel isexcluded as a potential negative stained nucleus center at step 1010.The method then terminates at step 1030.

However, if the pixel value is greater than the negative nucleusthreshold value at step 1008, the method proceeds to step 1012, wherethe pixel is labeled as a potential negative stained nucleus center.

In parallel, the negative stain image is filtered by the filter 406 ofthe distance masking module 404. The filter 406 removes low level noisefrom the negative stain image. At step 1016, the transform module 408applies a binary image generation process to the negative stain image toproduce a binary image.

At step 1018, the distance masking module 764 takes as input thepotential negative stained nucleus centers and the binary image andapplies distance masking to these inputs. The transform module 408applies a distance transform to the binary image to find the minimumdistance of each white pixel (value of 1) to any black pixel (value of0). Once the distance transform is complete, a distance image isgenerated with the distance value as the pixel value. For each potentialnegative stained nucleus center pixel identified in the blobidentification module 762, the corresponding distance value in the samelocation is added onto the potential negative stained nucleus centerpixel to generate a new image according to the following equation:

${P_{new}\left( {i,j} \right)} = \left\{ \begin{matrix}{\overset{\rightharpoonup}{P\left( {\iota,J} \right)} + {D\left( {i,j} \right)}} & {{if}\mspace{14mu}\overset{\rightharpoonup}{{P\left( {\iota,J} \right)}\mspace{11mu}}\;{is}\mspace{14mu} a\mspace{14mu}{potential}\mspace{14mu}{nucleus}\mspace{14mu}{center}} \\0 & {Otherwise}\end{matrix} \right.$

Each pixel in the newly generated image is compared to its neighbors.The local maximum pixel is identified as a negative stained nucleuscenter. The S×S area surrounding it is identified as the negativenucleus area and output as negative nucleus identification information,and negative stained nucleus centers are identified in step 1020. Themethod 1000 terminates at step 1030.

FIG. 11 is an illustration of a flow diagram for a method 1100 forpositive nuclei identification in a positive stain image according toexemplary embodiments of the present invention. The method 1100 is animplementation of the positive nucleus identification module (PNIM) 108shown in FIG. 1, implemented as the PNIM 728 in FIG. 7 as executed bythe processor 702.

The method 1100 begins at step 1102 and proceeds to step 1104. At step1104, the average intensity 510 in the positive stain image isdetermined by the average intensity module 772. At step 1106, a windowaveraging procedure is applied to the positive stain image produced bythe earlier stain separation, by the blob identification module 774.

The pixel values obtained through the window averaging are then invertedat step 1108 and compared to a content adaptive positive nucleusthreshold pixel value at step 1110. If the value at the current pixel isnot greater than the positive nucleus threshold value, the current pixelis excluded as a potential positive stained nucleus center at step 1112.The method then terminates at 1118.

However, if the pixel value is greater than the positive nucleusthreshold value at step 1110, the method proceeds to step 1114, wherethe pixel is labeled as a potential positive stained nucleus center.Each potential positive stained nucleus center pixel is compared to itsneighbors. The local maximum pixel is identified as a positive stainednucleus center. The S×S area surrounding it is identified as thepositive nucleus area and output as positive nucleus identificationinformation, and positive stained nucleus centers are identified in step1116. The method 1100 terminates at step 1118.

FIG. 12 is an illustration of a flow diagram for a method 1200 fordetermination of proportion score for a slide image according to anexemplary embodiment of the present invention. The method 1200 is animplementation of proportion scoring module 112 shown in FIG. 1,implemented as the proportion scoring module 732 in FIG. 7 as executedby the processor 702. The method begins at step 1202 and proceeds tostep 1204.

At step 1204, the proportion scoring module 732 determines a negativenucleus count according to the negative nucleus identificationinformation 410 received from the NNIM 730. At step 1206, the proportionscoring module 732 determines a positive nucleus count according to thepositive nucleus identification information 520 received from the PNIM108. At step 1208 a positive nucleus percent is computed. According tosome embodiments, the positive nucleus count and the negative nucleuscount is summed to obtain the total nuclei count. Then, the total nucleicount is used by the proportion scoring module 732 to compute thepositive nucleus percentage.

Proportion scores (PS) are then produced by the proportion scoringmodule 732 based on the scoring guideline provided by the AmericanSociety of Clinical Oncology and the College of American Pathologists.According to the scoring guidelines, the PS is assigned to a slide imagebased on the positive nucleus to total nucleus percentage.

At step 1210, it is determined whether the positive nucleus percentageis greater than 66.6%. If the positive nucleus percentage is greaterthan 66.6%, a proportion score of 5 is assigned to the slide image atstep 1211. If not, the method 1200 proceeds to step 1212.

At step 1212, it is determined whether the positive nucleus percentageis greater than 33.3%. If the positive nucleus percentage is greaterthan 33.3%, a proportion score of 4 is assigned to the slide image atstep 1213. If not, the method 1200 proceeds to step 1214.

At step 1214, it is determined whether the positive nucleus percentageis greater than 10%. If the positive nucleus percentage is greater than10%, a proportion score of 3 is assigned to the slide image at step1215. If not, the method 1200 proceeds to step 1216.

At step 1216, it is determined whether the positive nucleus percentageis greater than 1%. If the positive nucleus percentage is greater than1%, a proportion score of 2 is assigned to the slide image at step 1217.If not, the method 1200 proceeds to step 1218.

At step 1218, it is determined whether the positive nucleus percentageis greater than 0%. If the positive nucleus percentage is greater than0%, a proportion score of 1 is assigned to the slide image at step 1219.If the positive nucleus percentage is not greater than 0%, a proportionscore of 0 is assigned to the slide image at step 1220. The method 1200then terminates at step 1221.

FIG. 13 is an illustration for determining a Total Score (TS) accordingto exemplary embodiments of the present invention. The TS score iscomputed by the scoring module 100 of FIG. 1 using a PS 1310, such asthe PS obtained in the method 1200 and the average intensity 510obtained from the intensity analysis module 502. The scoring module 100determines an intensity score (IS) 1320 for the slide image based on theaverage intensity according to scoring guideline provided by theAmerican Society of Clinical Oncology and the College of AmericanPathologists. The TS score is obtained by the scoring module 100 bysumming the PS and IS as depicted FIG. 13.

Accordingly, the present invention may be realized in hardware, or acombination of hardware and software. The present invention may berealized in a centralized fashion in at least one computer system or ina distributed fashion where different elements may be spread acrossseveral interconnected computer systems. Any kind of computer system orother apparatus adapted for carrying out the methods described hereinmay be suited. A combination of hardware and software may be ageneral-purpose computer system with a computer program that, when beingloaded and executed, may control the computer system such that itcarries out the methods described herein. The present invention may berealized in hardware that comprises a portion of an integrated circuitthat also performs other functions.

The present invention may also be embedded in a computer programproduct, which comprises all the features enabling the implementation ofthe methods described herein, and which when loaded in a computer systemis able to carry out these methods. Computer program in the presentcontext means any expression, in any language, code or notation, of aset of instructions intended to cause a system having an informationprocessing capability to perform a particular function either directlyor after either or both of the following: a) conversion to anotherlanguage, code or notation; b) reproduction in a different materialform.

While the present invention has been described with reference to certainembodiments, it will be understood by those skilled in the art thatvarious changes may be made and equivalents may be substituted withoutdeparting from the scope of the present invention. In addition, manymodifications may be made to adapt a particular situation or material tothe teachings of the present invention without departing from its scope.Therefore, it is intended that the present invention not be limited tothe particular embodiment disclosed, but that the present invention willinclude all embodiments falling within the scope of the appended claims.

What is claimed is:
 1. A computer-implemented method for determinationof a cancer diagnosis score of a tissue sample comprising: applyingoptical domain transformations to a slide image of a tissue sample togenerate an optical domain transformed image; separating the slide imageinto a positive stain image and a negative stain image by performingstain separation on the optical domain transformed image; identifying apositive stained nucleus in the positive stain image of the tissuesample; identifying a negative stained nucleus in the negative stainimage; computing a proportion score based on a number of the positivestained nucleus identified and a number of the negative stained nucleusidentified; and determining the cancer diagnosis score based on theproportion score.
 2. The method of claim 1, further comprising:selecting a salient region of the slide image; and performing stainseparation in the salient region to obtain a positive stain image and anegative stain image, wherein identifying the positive stained nucleusis performed on the positive stain image and identifying the negativestained nucleus is performed on the negative stain image.
 3. The methodof claim 2, further comprising determining an average intensity ofpositive stain in the positive stain image.
 4. The method of claim 2,wherein selecting a salient region further comprises: performing asaliency analysis on the slide image to find a region rich in both redand blue channels, wherein the region found is the salient region. 5.The method of claim 4, wherein the stain separation further comprises:calculating stain vectors based on the saliency analysis for the salientregion.
 6. The method of claim 2, wherein the positive stain image andthe negative stain image are analyzed to obtain a size range parameterbased on a proportion of positive stained pixels with pixel intensitybelow a predetermined positive threshold and negative stained pixelswith pixel intensity below a predetermined negative threshold.
 7. Themethod of claim 1, where identifying a positive stained nucleuscomprises local maximum processing of the identified positive stainednucleus and identifying a negative stained nucleus comprises localmaximum processing of the identified negative stained nucleus.
 8. Themethod of claim 6, wherein identifying the negative stained nucleuscomprises: applying a window averaging procedure to each pixel in thenegative stain image based on the size range parameter; applying aninverse process on each pixel to obtain an inverse pixel and assigningthe pixel value to the inverse pixel value; determining a pixel to be anucleus center if the pixel value is greater than a predeterminednegative nucleus threshold; and excluding a pixel as a negative stainednucleus center if the pixel value is equal or less than thepredetermined negative nucleus threshold.
 9. The method of claim 8,where applying the inverse process further comprises receiving as aninput a window averaging of the intensity of the pixels of each of thepositive and negative stain images.
 10. The method of claim 8 whereinthe window averaging procedure is performed according to the equation:${\overset{\rightharpoonup}{P\left( {\iota,J} \right)} = {\frac{1}{S \times S}{\sum\limits_{i = 1}^{S}\;{\sum\limits_{j = 1}^{S}\;{P\left( {{i - \frac{S}{2}},{j - \frac{S}{2}}} \right)}}}}},$where S is the size range parameter and used as a side length of awindow over the negative stain image.
 11. The method of claim 6 whereinidentifying the negative stained nucleus further comprises: filteringthe negative stain image using a low pass filter to generate a filterednegative stain image; applying a binary image generation process on eachpixel of the filtered negative stain image to produce a binary image;applying a distance transform to the binary image to find a minimumdistance between white and black pixels by generating a distance imagewith the minimum distance as a pixel value; and generating a new imagewhere each pixel value is the sum of the value at a center pixel of thenegative stained nucleus and distance image pixel.
 12. The method ofclaim 6, wherein identifying the positive stained nucleus comprises:applying a window averaging procedure to each pixel in the positivestain image based on the size range parameter; applying an inverseprocess on each pixel to obtain an inverse pixel and assigning the pixelvalue to the inverse pixel value determining a pixel to be a nucleuscenter when the pixel value is greater than a positive nucleusthreshold; and excluding a pixel as a nucleus center when the pixelvalue is equal or less than the positive nucleus threshold.
 13. Themethod of claim 12, wherein the positive nucleus threshold is based onan average intensity of positive stain.
 14. The method of claim 12wherein the window averaging procedure is performed according to theequation:${\overset{\rightharpoonup}{P\left( {\iota,J} \right)} = {\frac{1}{S \times S}{\sum\limits_{i = 1}^{S}\;{\sum\limits_{j = 1}^{S}\;{P\left( {{i - \frac{S}{2}},{j - \frac{S}{2}}} \right)}}}}},$where S is the size range parameter and used as a side length of awindow over the positive stain image.
 15. The method of claim 1,computing proportion score further comprising: summing number of thepositive stained nucleus identified and the negative stained nucleusidentified to obtain a total nuclei count; and computing a positivestained nuclei percentage based on number of the positive stainednucleus identified and the total nuclei count.
 16. The method of claim15, further comprising: assigning a proportion score of 5 when thepositive stained nuclei percentage is more than 66.6%; assigning aproportion score of 4 when the positive stained nuclei percentage ismore than 33.3%; assigning a proportion score of 3 when the positivestained nuclei percentage is more than 10%; assigning a proportion scoreof 2 when the positive stained nuclei percentage is more than 1%;assigning a proportion score of 1 when the positive stained nucleipercentage is more than 0%; and assigning a proportion score of 0 whenthe positive stained nuclei percentage is 0%.
 17. The method of claim 3wherein the cancer diagnosis score is an estrogen receptor orprogesterone receptor (ER/PR) score and the cancer marker is ER or PRrespectively.
 18. The method of claim 17 further comprising: determiningthe ER/PR score by summing the proportion score and an intensity scorebased on the average intensity.
 19. An apparatus for determining acancer diagnosis score comprising: an interest area module that extractsa user interest area from a slide image of a tissue sample and appliesoptical domain transformations to the slide image to generate an opticaldomain transformed image; a separation module that separates the opticaldomain transformed image into a positive stain image and a negativestain image; a positive nucleus identification module for identifying apositive stained nucleus in the positive stain image; a negative nucleusidentification module for identifying a negative stained nucleus in thenegative stain image; a proportion scoring module for computing aproportion score based on number of the positive stained nucleusidentified and number of the negative stained nucleus identified; and ascoring module for determining the cancer diagnosis score based on theproportion score.
 20. A computer-implemented method for determination ofa cancer diagnosis score of a tissue sample comprising: separating aslide image into a positive stain image and a negative stain image viaan optical domain transformation; identifying a positive stained nucleusin the positive stain image by local maximum processing of theidentified positive stained nucleus; identifying a negative stainednucleus in the negative stain image by local maximum processing of theidentified negative stained nucleus; computing a proportion score basedon a number of the positive stained nucleus identified and a number ofthe negative stained nucleus identified; and determining the cancerdiagnosis score based on the proportion score.